What makes this different

Most AI trading projects: feed data into a model, get a signal, done.

Orallexa runs a full adversarial pipeline. A Bull AI argues for the trade. A Bear AI argues against it. A Judge AI makes the final call with evidence from both sides. Then it executes.

Market Data → 9 ML Models → Bull/Bear Debate → Judge Verdict  → Risk Plan → Paper Execution → Dashboard → Social Content

Market Data → 9 ML Models → Bull/Bear Debate → Judge Verdict  → Risk Plan → Paper Execution → Dashboard → Social Content

Every stage automated. Every stage observable. The system runs continuously.

Try it instantly

Open Live Demo — demo mode, no API key needed. Click NVDA, TSLA, or QQQ to see a full analysis.

Or run locally:

git clone https://github.com/alex-jb/orallexa-ai-trading-agent.git cd orallexa-ai-trading-agent pip install -r requirements.txt echo "ANTHROPIC_API_KEY=your_key" > .env

git clone https://github.com/alex-jb/orallexa-ai-trading-agent.git cd orallexa-ai-trading-agent pip install -r requirements.txt echo "ANTHROPIC_API_KEY=your_key" > .env

Terminal 1: API

python api_server.py

Terminal 2: UI

cd orallexa-ui && npm install && npm run dev`

Docker: docker compose up --build — that's it.

Walk-Forward Evaluation (Out-of-Sample)

Strategy Ticker OOS Sharpe Info Ratio MC Pct p-value

rsi_reversal INTC 1.41 0.45 43.4% 0.002

alpha_combo JPM 1.11 -1.26 97.4% 0.135

trend_momentum JPM 1.09 -0.80 90.2% 0.104

macd_crossover JPM 0.99 -1.02 100% 0.236

dual_thrust NVDA 0.96 -0.93 89.4% 0.001

70 strategy-ticker pairs evaluated. Top 5 by OOS Sharpe shown. The value is in the ML ensemble + LLM synthesis layer above rule-based strategies. Full report →

Architecture

Intelligence Layer

Component Detail

9 ML Models RF, XGB, EMAformer, MOIRAI-2, Chronos-2, DDPM, PPO RL, GNN, LR

Adversarial Debate Bull/Bear/Judge via Claude Sonnet + Haiku

Strategy Evolution LLM generates Python strategies → sandbox tests → evolves winners

Daily Intel 50+ tickers, sector rotation, volume spikes, AI morning brief

Execution Layer

Component Detail

Paper Trading Alpaca bracket orders with auto stop-loss/take-profit

Real-time Stream WebSocket prices every 5s + signal change alerts

Dashboard Next.js 16, Art Deco theme, EN/ZH bilingual

Desktop Coach Floating AI pet with voice input (Whisper) + TTS

Example Output

What one NVDA analysis produces:

┌─────────────────────────────────────────────────────────────────┐ │ DECISION: BUY Confidence: 68% │ │ Risk: MEDIUM Signal: 72/100 │ ├─────────────────────────────────────────────────────────────────┤ │ │ │ BULL CASE: │ │ • Price above MA20 > MA50 — full bullish alignment │ │ • RSI at 62 — strong momentum, not yet overbought │ │ • Volume 1.8x average — institutional participation likely │ │ │ │ BEAR CASE: │ │ • ADX at 32 but declining — trend may be exhausting │ │ • Bollinger %B at 0.85 — extended near upper band │ │ • Earnings in 12 days — vol crush after event │ │ │ │ JUDGE VERDICT: │ │ "Bull case is stronger. BUY with tight stop at MA20." │ │ │ │ PROBABILITIES: Up 58% | Neutral 24% | Down 18% │ │ RISK PLAN: │ │ Entry: $132.50 | Stop: $128.40 | Target: $141.00 | R:R 2.1:1 │ └─────────────────────────────────────────────────────────────────┘

┌─────────────────────────────────────────────────────────────────┐ │ DECISION: BUY Confidence: 68% │ │ Risk: MEDIUM Signal: 72/100 │ ├─────────────────────────────────────────────────────────────────┤ │ │ │ BULL CASE: │ │ • Price above MA20 > MA50 — full bullish alignment │ │ • RSI at 62 — strong momentum, not yet overbought │ │ • Volume 1.8x average — institutional participation likely │ │ │ │ BEAR CASE: │ │ • ADX at 32 but declining — trend may be exhausting │ │ • Bollinger %B at 0.85 — extended near upper band │ │ • Earnings in 12 days — vol crush after event │ │ │ │ JUDGE VERDICT: │ │ "Bull case is stronger. BUY with tight stop at MA20." │ │ │ │ PROBABILITIES: Up 58% | Neutral 24% | Down 18% │ │ RISK PLAN: │ │ Entry: $132.50 | Stop: $128.40 | Target: $141.00 | R:R 2.1:1 │ └─────────────────────────────────────────────────────────────────┘

Not just a number. A structured argument with transparent reasoning and an actionable risk plan.

9 ML Models — Scored and Ranked

Every analysis runs all available models. The ML Scoreboard shows Sharpe, return, win rate side by side.

Model Type What It Does

Random Forest Classification 28 technical features → 5-day direction

XGBoost Gradient Boosting Same features, different optimization

Logistic Regression Linear Regularized baseline

EMAformer Transformer iTransformer + Embedding Armor (AAAI 2026)

MOIRAI-2 Foundation Salesforce zero-shot time series forecaster

Chronos-2 Foundation Amazon T5-based probabilistic forecaster

DDPM Diffusion Generative 50 possible price paths → VaR and confidence intervals

PPO RL Agent Reinforcement Gymnasium env, Sharpe-based reward

GNN (GAT) Graph 17-stock relationship graph, inter-stock signal propagation

All models run on CPU.

Signal View — Decision card, probability bars, Bull/Bear debate, ML scoreboard, risk plan. Intel View — Morning brief, gainers/losers, sector heatmap, volume spikes, AI picks, social thread.

Art Deco theme. Polymarket-inspired probability display. Mobile responsive. EN/ZH bilingual.

Desktop AI Coach

A floating pixel bull that lives on your desktop:

• Voice chat — Hold K to talk, Whisper transcribes, Claude responds

• Chart analysis — Ctrl+Shift+S screenshots any chart for Claude Vision analysis

• Decision cards — Entry, stop, target, risk/reward overlaid on screen

• Market-aware avatar — Bull changes color based on market conditions

Cost-Aware AI

Not every task needs the expensive model:

Task Model Cost

Bull/Bear arguments Haiku 4.5 ~$0.001

Signal overlay Haiku 4.5 ~$0.001

Judge verdict Sonnet 4.6 ~$0.005

Deep market report Sonnet 4.6 ~$0.005

One full analysis: ~$0.003. One daily intel report: ~$0.05.

Why this architecture

Problem Typical Approach Orallexa

Isolated signals One model, one prediction 9 models ranked by Sharpe + LLM synthesis

No reasoning "BUY 73%" — why? Bull argues, Bear argues, Judge decides with evidence

Expensive AI Every call hits GPT-4 Haiku for 80%, Sonnet only where reasoning matters

Manual workflow Notebook → read → decide → execute Automated: signal → debate → risk plan → paper order

No context Each stock analyzed alone GNN propagates signals across 17 related stocks

Not shareable Screenshot your terminal "Copy for X" on every section

Tech Stack

FrontendNext.js 16, React 19, Tailwind CSS 4, PWA BackendFastAPI, Python 3.11, WebSocket AIClaude Sonnet 4.6 + Haiku 4.5 (dual-tier routing) MLscikit-learn, XGBoost, PyTorch (EMAformer, DDPM, GAT, PPO) Datayfinance (real-time + historical) NLPFinBERT, VADER, TextBlob TradingAlpaca paper trading (bracket orders) OrchestrationLangGraph (stateful debate pipeline) DeployDocker, GitHub Actions CI/CD, Vercel

Testing

277 automated tests. 0 failures. CI on every push.

python -m pytest tests/ -v # Backend (113 tests) cd orallexa-ui && npm test # Frontend (139 tests)

python -m pytest tests/ -v # Backend (113 tests) cd orallexa-ui && npm test # Frontend (139 tests)

Full test breakdown

Suite Tests Coverage

Engine Integration 34 TA indicators, strategies, backtest, brain routing

ML Regression 13 All 9 models — ensures upgrades don't degrade

API E2E 19 Every endpoint via FastAPI TestClient

Unit Tests 47 DecisionOutput, BehaviorMemory, risk, scalping

Types & Helpers 28 Display functions, color mapping, i18n

Components 67 DecisionCard, Breaking, MarketStrip, ML Scoreboard, Watchlist, DailyIntel

Mock Data 31 All mock generators

API

Endpoints

Method Endpoint Description

POST /api/analyze Fast signal analysis (scalp/intraday/swing)

POST /api/deep-analysis Multi-agent deep analysis with debate

POST /api/chart-analysis Screenshot chart analysis (Claude Vision)

POST /api/watchlist-scan Parallel multi-ticker scan

GET /api/daily-intel Daily market intelligence (cached)

GET /api/news/{ticker} News + sentiment scores

GET /api/profile Trader behavior profile

GET /api/journal Decision execution log

POST /api/evolve-strategies LLM strategy evolution

GET /api/alpaca/account Paper trading account

POST /api/alpaca/execute Execute signal as paper order

WS /ws/live Real-time price + signal stream

Project Structure

Directory layout

orallexa/ ├── api_server.py # FastAPI + WebSocket server ├── docker-compose.yml # One-click deployment │ ├── engine/ # Trading engine (9 models) │ ├── multi_agent_analysis.py # LangGraph debate pipeline │ ├── ml_signal.py # Model comparison framework │ ├── strategies.py # 7 rule-based strategies │ ├── emaformer.py # EMAformer Transformer │ ├── diffusion_signal.py # DDPM probabilistic forecasting │ ├── gnn_signal.py # Graph Attention Network │ ├── rl_agent.py # PPO reinforcement learning │ ├── strategy_evolver.py # LLM strategy evolution │ └── sentiment.py # FinBERT / VADER │ ├── llm/ # AI reasoning │ ├── claude_client.py # Dual-tier model routing │ ├── debate.py # Bull/Bear debate │ └── debate_graph.py # LangGraph pipeline │ ├── orallexa-ui/ # Dashboard (Next.js 16) ├── desktop_agent/ # Desktop AI coach ├── bot/ # Execution layer (Alpaca) ├── tests/ # 138 backend tests └── .github/workflows/ # CI/CD

orallexa/ ├── api_server.py # FastAPI + WebSocket server ├── docker-compose.yml # One-click deployment │ ├── engine/ # Trading engine (9 models) │ ├── multi_agent_analysis.py # LangGraph debate pipeline │ ├── ml_signal.py # Model comparison framework │ ├── strategies.py # 7 rule-based strategies │ ├── emaformer.py # EMAformer Transformer │ ├── diffusion_signal.py # DDPM probabilistic forecasting │ ├── gnn_signal.py # Graph Attention Network │ ├── rl_agent.py # PPO reinforcement learning │ ├── strategy_evolver.py # LLM strategy evolution │ └── sentiment.py # FinBERT / VADER │ ├── llm/ # AI reasoning │ ├── claude_client.py # Dual-tier model routing │ ├── debate.py # Bull/Bear debate │ └── debate_graph.py # LangGraph pipeline │ ├── orallexa-ui/ # Dashboard (Next.js 16) ├── desktop_agent/ # Desktop AI coach ├── bot/ # Execution layer (Alpaca) ├── tests/ # 138 backend tests └── .github/workflows/ # CI/CD

Acknowledgments

Anthropic Claude · yfinance · Polymarket · Alpaca

MIT License — see LICENSE

Disclaimer: Research and educational project. Not financial advice.

Built with conviction, not hype.